激光雷达无人飞行器自主着陆选址方法

无人飞行器在陌生场景中的自主路径规划与着陆工作一直是相关领域研究的重点,提出了一种基于激光雷达采集到的 点云数据来给飞行器推荐最优着陆地址的方法。 该方法通过飞行器位姿信息辅助以及选址窗口维护等手段校正并扩充了雷达 采集到的原始点云数据,之后再利用改进的 RANSAC 算法对生成的候选平面进行评估选择,最后反馈最优着陆点的位置坐标 信息。 仿真环境下的实验结果表明该方法运算结果稳定准确,且运算速度满足飞行器实时工作需求,同时计算所需空间时间开 销较小,能够符合在现实场景中正常工作的标准。

Landing site recommendation for unmanned aerial vehicles based on lidar data

The autonomous path planning and landing of unmanned aerial vehicle in open scenes has been the focus of research in related fields. A method based on the point cloud data collected by the lidar to recommend the optimal landing address for the UAV is proposed. This method corrects and expands the original point cloud data by using the UAV pose information and maintaining the site selection window, then the RANSAC algorithm is improved to evaluate and select the generated candidate planes, and finally output the coordinate information of the optimal landing site. The experiments in the simulation environment show that the results of this method are stable and accurate, and the computational speed satisfies the requirements of the UAV to work in real time. At the same time, the space and time cost of this method is light, which meets the standard of the operation in real scenes.